Multi-Objective Particle Swarm Optimization Algorithm based on Position Vector Offset

Abstract

In response to the issue that particle swarm optimization algorithms tend to fall into local optima when dealing with multi-objective optimization tasks, a multi-objective optimization algorithm based on particle swarm is proposed. This algorithm is based on the relationship between the position vectors of particles, changing the selection and movement strategies of particles to find the true Pareto front. Firstly, two additional position vectors are added around the iterating particles to enhance their search capability; then, a non-dominated vector archive is established to record the non-dominated solutions of the iterating particles and the additional position vectors, increasing particle diversity. Finally, additional position vectors with high fitness are selected to produce a shift in the iterating particle's position, accelerating particle convergence. Comparing this algorithm with dMOPSO, SMPSO, NMPSO, and MOPSOCD algorithms, simulation experiments show that the proposed PVSPSO algorithm has stronger optimization ability.